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20242026
most citedSA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing

5 citations · 7 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024★ 1 cited

Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving

Kairui Ding, Boyuan Chen, Yuchen Su +8

End-to-end architectures in autonomous driving (AD) face a significant challenge in interpretability, impeding human-AI trust. Human-friendly natural language has been explored for…

cs.CV2024

FairDiff: Fair Segmentation with Point-Image Diffusion

Wenyi Li, Haoran Xu, Guiyu Zhang +4

Fairness is an important topic for medical image analysis, driven by the challenge of unbalanced training data among diverse target groups and the societal demand for equitable med…

cs.RO2024★ 1 cited

PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments

Kairui Ding, Boyuan Chen, Ruihai Wu +8

Robotic manipulation with two-finger grippers is challenged by objects lacking distinct graspable features. Traditional pre-grasping methods, which typically involve repositioning…

cs.CV2024★ 5 cited

SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing

Xiaowei Song, Jv Zheng, Shiran Yuan +5

In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs modifying the training procedu…

cs.CV2024

SCP-Diff: Spatial-Categorical Joint Prior for Diffusion Based Semantic Image Synthesis

Huan-ang Gao, Mingju Gao, Jiaju Li +4

Semantic image synthesis (SIS) shows good promises for sensor simulation. However, current best practices in this field, based on GANs, have not yet reached the desired level of qu…